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Sewon Min
Sewon Min
UC Berkeley EECS & Allen Institute for AI
Dirección de correo verificada de cs.washington.edu - Página principal
Título
Citado por
Citado por
Año
Dense Passage Retrieval for Open-Domain Question Answering
V Karpukhin, B Oğuz, S Min, L Wu, S Edunov, D Chen, W Yih
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
38312020
Rethinking the role of demonstrations: What makes in-context learning work?
S Min, X Lyu, A Holtzman, M Artetxe, M Lewis, H Hajishirzi, L Zettlemoyer
arXiv preprint arXiv:2202.12837, 2022
13492022
Unifiedqa: Crossing format boundaries with a single qa system
D Khashabi, S Min, T Khot, A Sabharwal, O Tafjord, P Clark, H Hajishirzi
Findings of Empirical Methods in Natural Language Processing (EMNLP), 2020
756*2020
Replug: Retrieval-augmented black-box language models
W Shi, S Min, M Yasunaga, M Seo, R James, M Lewis, L Zettlemoyer, ...
arXiv preprint arXiv:2301.12652, 2023
544*2023
Factscore: Fine-grained atomic evaluation of factual precision in long form text generation
S Min, K Krishna, X Lyu, M Lewis, W Yih, PW Koh, M Iyyer, L Zettlemoyer, ...
arXiv preprint arXiv:2305.14251, 2023
5212023
Measuring and narrowing the compositionality gap in language models
O Press, M Zhang, S Min, L Schmidt, NA Smith, M Lewis
arXiv preprint arXiv:2210.03350, 2022
495*2022
Metaicl: Learning to learn in context
S Min, M Lewis, L Zettlemoyer, H Hajishirzi
arXiv preprint arXiv:2110.15943, 2021
4662021
AmbigQA: Answering Ambiguous Open-domain Questions
S Min, J Michael, H Hajishirzi, L Zettlemoyer
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
2792020
Multi-hop Reading Comprehension through Question Decomposition and Rescoring
S Min, V Zhong, L Zettlemoyer, H Hajishirzi
Annual Meeting of the Association for Computational Linguistics (ACL), 2019
2692019
Efficient and Robust Question Answering from Minimal Context over Documents
S Min, V Zhong, R Socher, C Xiong
Annual Meeting of the Association for Computational Linguistics (ACL), 2018
265*2018
Towards understanding chain-of-thought prompting: An empirical study of what matters
B Wang, S Min, X Deng, J Shen, Y Wu, L Zettlemoyer, H Sun
arXiv preprint arXiv:2212.10001, 2022
224*2022
Noisy channel language model prompting for few-shot text classification
S Min, M Lewis, H Hajishirzi, L Zettlemoyer
arXiv preprint arXiv:2108.04106, 2021
2192021
A Discrete Hard EM Approach for Weakly Supervised Question Answering
S Min, D Chen, H Hajishirzi, L Zettlemoyer
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019
1782019
Compositional Questions Do Not Necessitate Multi-hop Reasoning
S Min, E Wallace, S Singh, M Gardner, H Hajishirzi, L Zettlemoyer
Annual Meeting of the Association for Computational Linguistics (ACL), 2019
1532019
Efficient One-Pass End-to-End Entity Linking for Questions
BZ Li, S Min, S Iyer, Y Mehdad, W Yih
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
1512020
Retrieval-based language models and applications
A Asai, S Min, Z Zhong, D Chen
Proceedings of the 61st Annual Meeting of the Association for Computational …, 2023
144*2023
Question Answering through Transfer Learning from Large Fine-grained Supervision Data
S Min, M Seo, H Hajishirzi
Annual Meeting of the Association for Computational Linguistics (ACL), 2017
1382017
Query-reduction networks for question answering
M Seo, S Min, A Farhadi, H Hajishirzi
International Conference on Learning Representations (ICLR), 2017
137*2017
Knowledge guided text retrieval and reading for open domain question answering
S Min, D Chen, L Zettlemoyer, H Hajishirzi
arXiv preprint arXiv:1911.03868, 2019
1202019
Do membership inference attacks work on large language models?
M Duan, A Suri, N Mireshghallah, S Min, W Shi, L Zettlemoyer, Y Tsvetkov, ...
arXiv preprint arXiv:2402.07841, 2024
114*2024
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Artículos 1–20